We propose a hierarchical entity graph convolutional network (HEGCN) for the multi-hop relation extraction. We encode the documents in a document chain using a bi-directional long short-term memory (BiLSTM) layer [1]}. On top of the BiLSTM layer, we use two graph convolutional networks (GCN), one after another in a hierarchy. In the first level of the GCN hierarchy, we construct a separate entity mention graph on each document of the chain using all the entities mentioned in that document. Each mention of an entity in a document is considered as a separate node in the graph. We use a graph convolutional network (GCN) on the entity mention graph of each document to capture the relations among the entity mentions in the document. This mention-level graph helps to pass information among the entity mentions in a document. We then construct a unified entity-level graph across all the documents in the chain. Each node of this entity-level graph represents a unique entity in the document chain. Each common entity between two documents in the chain is represented by a single node in the graph. We use a GCN on this unified entity-level graph to capture the relations among the entities across the documents. This unified entity graph helps to pass information among the multiple documents. This graph is a local knowledge graph involving the entities present in the documents. GCN is used to enrich the representations of the entities in the graph. We concatenate the representations of the nodes of the subject entity and object entity and pass it to a feed-forward layer with softmax for relation classification.
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